Question:

During the last few years, many              algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.

A.

logical

B.

classical

C.

classification

D.

none of above

Answer» d. none of above

Question:

Techniques involve the usage of both labeled and unlabeled data is called     .

A.

supervised

B.

semi-supervised

C.

unsupervised

D.

none of the above

Answer» b. semi-supervised

Question:

Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

A.

a)since the there is a relationship means our model is not good

B.

b)since the there is a relationship means our model is good

C.

c)cant say

D.

d)none of these

Answer» a. a)�since the there is a relationship means our model is not good

Question:

overlearning causes due to an excessive           .

A.

capacity

B.

regression

C.

reinforcement

D.

accuracy

Answer» a. capacity

Question:

Even if there are no actual supervisors               learning is also based on feedback provided by the environment

A.

supervised

B.

reinforcement

C.

unsupervised

D.

none of the above

Answer» b. reinforcement

Question:

While using           all labels are turned into sequential numbers.

A.

labelencoder class

B.

labelbinarizer class

C.

dictvectorizer

D.

featurehasher

Answer» a. labelencoder class

Question:

Which of the following methods do we use to find the best fit line for data in Linear Regression?

A.

a)least square error

B.

b)maximum likelihood

C.

c) logarithmic loss

D.

d) both a and b

Answer» a. a)�least square error

Question:

While using feature selection on the data, is the number of features decreases.

A.

no

B.

yes

Answer» b. yes

Question:

Overfitting is more likely when you have huge amount of data to train?

A.

a) true

B.

b) false

Answer» b. b) false

Question:

Given that we can select the same feature multiple times during the recursive partitioning of
the input space, is it always possible to achieve 100% accuracy on the training data (given
that we allow for trees to grow to their maximum size) when building decision trees?

A.

yes

B.

no

Answer» b. no